This system is somewhat more complex than the previous one because, in this case, no prior information is given to the machine. Instead, it is the algorithms themselves that must identify the patterns that are repeated and organize them in a structured way.
However, they start from an unlabeled dataset , and this is the main difference from supervised learning. For example, this is the learning used in financial and banking institutions to identify fraudulent practices.
The last major category is reinforcement learning, whose main characteristic is that the algorithm learns associations from its own experience. What we commonly know as trial and error, transferred to the environment of electronic devices.
Returning to the banking example, reinforcement learning cameroon whatsapp data would make it possible to decide the best way to create a personalized investment portfolio without anyone intervening.
In which sectors will machine learning be leveraged ?
The financial sector is one of those that can benefit most from this technology, thanks to which it is possible to clearly and accurately identify fraud attempts , reduce the risk associated with different financial operations or investments , or create financial services with a high degree of personalization.
Health sector
The healthcare sector is also another area where the application of machine learning is allowing for rapid progress. For example, it allows for initial diagnoses to be made based on a patient's symptoms, which helps doctors narrow down their search and identify the health problem more quickly.
Online commerce
Commerce, especially online commerce , also uses machine learning mechanisms to study the behaviour patterns of its users and anticipate which offers to launch and when to do so. In addition, it analyses which products will be in greatest demand in future campaigns, which helps to segment messages and the target audience much better.
Logistics sector
Logistics uses machine learning to automate certain processes that help them gain productivity. It is also a key technology for any business that wants to offer more personalized attention to its customers, thanks to the application of instant messages that allow more interesting data to be collected to get to know customers better.
Human resources
Even Human Resources departments can use this technology to predict which workers in a company, both large and SME, will achieve greater productivity in a future period of time or to implement and develop the equality plan in companies.
Machine learning examples in your environment
Our daily lives are full of machine learning applications that we are not aware of. Some of the most common ones are:
Voice assistants: all devices capable of recognizing and processing the commands we transmit to them verbally do so thanks to machine learning .
Chatbots : If we enter a website where we see a pop-up window that opens a direct conversation with the company in question, we are looking at an example of machine learning . The program itself is trained to identify what we are saying and respond in a coherent and effective manner.
Google Searches: The reason why the suggestions that appear on search engines like Google are so personalized is because of the learning that each user has gained thanks to this technology.
Recommendations on Netflix or HBO: If you notice, the films and series that these types of digital platforms recommend to you are not the same as those recommended to another user close to you. The reason? They use machine learning to establish associations that allow them to recommend films or series similar to those you have already seen, and that the program understands that you like.
As you can see, there is a wide range of everyday elements in which machine learning is very present, even if we are not aware of it. In fact, it is one of the technologies that allows Grupo Caja Rural to offer financial services and products that always respond to the needs of our clients. A good example is the My Finances tool , available to all clients within Ruralvía, where they can automatically find an analysis of their financial capacity.